An adaptive filter for noise cancelling

Abutaleb, Ahmed S.

doi:10.1109/31.7595

Cited by 24 publications

(7 citation statements)

References 12 publications

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“…The Dirac filter: (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17) where /3 is a positive number called the decay constant.…”

Section: E^imentioning

confidence: 99%

“…• The calculation of the first one is straightforward. Indeed: 40 3 (3.48). Expression (3.48) also shows a very nice link between the condition number and the predictors of all orders.…”

Section: Fast Computation Of the Condition Numbermentioning

confidence: 99%

“…The reference signal is derived from one or more sensors located at points in the noise field where the signal is weak or undetectable. This technique is often referred to as adaptive noise cancellation (ANC) [215], [321], [320], [147], [209], [3], [306].…”

Section: Adaptive Noise Cancellationmentioning

confidence: 99%

“…Section 3.6 generalizes the Wiener filter to the MIMO system case. While this generalization is 32 3 Wiener Filter and Basic Adaptive Algorithms straightforward, the optimal solution does not always exist and identification problems may be possible only in some situations. Section 3.7 gives some numerical examples for the identification of SISO and MISO systems with the NLMS algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Acoustic MIMO Signal Processing

Chen¹,

Huang²,

Benesty³

2006

Signals and Communication Technology

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“…The Dirac filter: (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17) where /3 is a positive number called the decay constant.…”

Section: E^imentioning

confidence: 99%

“…• The calculation of the first one is straightforward. Indeed: 40 3 (3.48). Expression (3.48) also shows a very nice link between the condition number and the predictors of all orders.…”

Section: Fast Computation Of the Condition Numbermentioning

confidence: 99%

Section: Adaptive Noise Cancellationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Acoustic MIMO Signal Processing

Chen¹,

Huang²,

Benesty³

2006

Signals and Communication Technology

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“…Clearly both &(PO, -I, aw, 6 ) and P k , k -1 (PO, -I , ow, uu) also depend on the other matrices of the state space representation (6), (7). For simplicity of notation this dependence has not been explicitly indicated.…”

Section: Estimation Of U; and Amentioning

confidence: 99%

Low a priori statistical information model for optimal smoothing and differentiation of noisy signals

Fioretti

Jetto

1994

Adaptive Control & Signal

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A novel approach is proposed to the optimal smoothing and differentiation problem of unknown onedimensional signals corrupted by additive white Gaussian noise. State space techniques are used. The main feature of the method is that very little a priori statistical information about the signal generation process is required. Starting from the basic assumption that the actual waveform does not show relevant discontinuities, a state space representation is derived by defining a state vector composed of the signal and its derivatives. All parameters of this representation are analytically derived except two: a multiplicative scalar of the input noise covariance matrix and the variance of the measurement noise. A procedure for the optimal estimation of these parameters from noisy data is proposed. Application of a fixed-lag Kalman smoother provides the simultaneous estimate of the signal and its derivatives. Numerical results confirm the validity of the approach.

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